The machines have taken over; the future of the Internet of Things is already here. Across a dizzying array of applications, machines are generating copious quantities of data, most of it being pushed to the cloud for storage, processing and analysis.

As Amazon CTO Werner Vogels told attendees at the MongoDB World conference, however, we may not always like the consequences of the Big Data that we’re producing.

Big Data? Big Deal

Data today is very big, but not because it resides on mammoth mainframes or resides on huge, centralized databases. Nor is it big because any particularly large company is creating it. It’s big, rather, because we live in a new machine age, with a vast proliferation of machines emitting data in volumes and variety the world has never seen.

How big? Well, it’s getting hard to impress anyone with Big Data statistics, but Vogels tried anyway. Two of the statistics he shared are, in fact, impressive, if not scary, because they illustrate just how out-of-control IoT data has become:

The amount of information generated during the first day of a baby’s life today is equivalent to 70 times the information contained in the Library of Congress, due not only to hospital equipment capturing data but also proud parents toting cameras, video recorders and more;

Dropcam, a 5-year old startup with under $60 million in revenue just acquired by Google’s Nest unit, has more data uploaded per minute than YouTube, processing petabytes of data every month as its cameras serve as baby monitors and home security systems.

As organizations learn to harness this data, some of it is proving incredibly useful. Retail outlets, for example, are finding ways to minimize customer churn and to make it easier for customers to find the products or special deals they’re looking for. Other uses have less business value and simply have novelty value.

Sport’s Dismal Science

And yet Vogels pointed to other uses that may interrupt the natural flow of our lives in ways that seem to make them better, but actually don’t. Take sports, for example.

As much as we may admire LeBron James for his ability to consistently make baskets, or Luis Suarez’s ability to put the ball into the back of opponents’ nets (and not so much his teeth into their arms or shoulders), much of the joy of sports comes from watching human intuition in play.

IoT data can help to augment that intuition, of course. For example, scientists have discovered teammates’ hearts beat in rhythm when they’re playing well together, as shown below. Coaches can pull a player who seems to be out of sync with the rest of the team, not by measuring their performance but rather their heartbeat:

Maybe this is OK. But supposed it’s a data scientist who determines the optimal shot as the game clock winds down, rather than James?

“With connected data,” Vogels says, “a shot is not something you do by looking in your competitor’s eyes. You do it because a data scientist told you to.” It might win the game, but in the process it might also takes something essential from the game. By turning sport into science, we threaten to ruin the fun of it.

Maybe It’s Benign—Maybe

Despite the potential for misuse—whether for nefarious ends or simply for purposes that may remove a bit of the joy of life—Vogels celebrated a large catalog of current uses for IoT data. We too often get fixated on having our refrigerator tell us to get more milk, when there are far more powerful reasons to use IoT data: DNA sequencing, energy conservation, space exploration and more.

Still, instead of rushing blindly into this brave new world of Big Data, we would do well to remember Big Data is not always better. Sometimes it’s precisely the messiness of life that makes it worthwhile.